{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#-*- coding:utf-8 -*-\n",
    "# 画谱系聚类图\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# 参数初始化\n",
    "filename = '1_1standardization.xlsx'\n",
    "data = pd.read_excel(filename, index_col = u'基站编号')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "# 这里使用scipy的层次聚类函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rvtrWoNMsbSw7rduGY9XgMdYj7t7IEwV0/fpwfSuLRQMDIW2r3lYUw/Xrw/9Y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\n7kIIUUAk7kIIUUAk7kIIUUD+P8mEJJ/sV8feAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4a31b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = linkage(data, method = 'weighted', metric = 'euclidean') # method = 'weighted'时的谱系聚类图\n",
    "\n",
    "p = dendrogram(z, 0) # 画谱系聚类图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+Ji1eGy52wrh7pn4Pk3edYud5IZ0pzp0nebIpz3Te6R5hp1ptevy689iyZdPr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\ndyGEKCASdyGEKCD/PwOEqeb0WKrVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa0f0cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = linkage(data, method = 'single', metric = 'euclidean') # method = 'single'谱系聚类图 \n",
    "\n",
    "p = dendrogram(z, 0) # 画谱系聚类图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6yydTYSLlUcaCPXzyJKOVmfdRN/Au7ciCd1ttQrJe51EBxqemGA+2cqqABztH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\nMXE3DMMoICbuhmEYBcTE3TAMo4D8f5P82cR5yNAcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9f9b4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = linkage(data, method = 'complete', metric = 'euclidean') # method = 'complete'谱系聚类图 \n",
    "\n",
    "p = dendrogram(z, 0) # 画谱系聚类图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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61TEEkQ5/FNm90ojaxASjQH9GnFvNc+OgkBbfNOkBLS3u6UEnzgT6bNaRzntf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\nwICmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xccd7da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = linkage(data, method = 'average', metric = 'euclidean') # method = 'average'谱系聚类图 \n",
    "\n",
    "p = dendrogram(z, 0) # 画谱系聚类图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e3hCXj+3YBjHG23T2RBCXRyYn6XKOU95XvVIIIr1nYoLx6emmvzKNXuihcpmD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\nyFl2rgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4a31e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = linkage(data, method = 'centroid', metric = 'euclidean') # method = 'centroid'谱系聚类图 \n",
    "\n",
    "p = dendrogram(z, 0) # 画谱系聚类图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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50wrnM7SUXjOoRsNBNQpP9u/sQmI9SqVz84nEh5myIYpqHjmEhd7Ihg21Zxjp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\ncojEXQghcojEXQghcsj/B86v3DQ3vAsDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xccd7a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = linkage(data, method = 'median', metric = 'euclidean') # method = 'median'谱系聚类图 \n",
    "\n",
    "p = dendrogram(z, 0) # 画谱系聚类图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4Yy2+xZUn43m4qJ2DbzGtirZ4JT7AluMD7Pei/F/EB+gjUY/hqP+juIjux8Xg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\n4f8DwpN2N3dJQ0YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcea8b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = linkage(data, method = 'ward', metric = 'euclidean') # method = 'ward'谱系聚类图 # Ward方差最小化算法\n",
    "\n",
    "p = dendrogram(z, 0) # 画谱系聚类图\n",
    "plt.savefig('puxijulei.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
       "            connectivity=None, linkage='ward', memory=None, n_clusters=3,\n",
       "            pooling_func=<function mean at 0x000000000627D908>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 由谱系聚类图可知，聚类类别为3类\n",
    "# 层次聚类算法\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# 参数初始化\n",
    "filename = '1_1standardization.xlsx'\n",
    "data = pd.read_excel(filename, index_col = u'基站编号')\n",
    "k = 3 # 聚类数\n",
    "\n",
    "from sklearn.cluster import AgglomerativeClustering # 导入sklearn的层次聚类函数\n",
    "model = AgglomerativeClustering(n_clusters = k, linkage = 'ward')\n",
    "model.fit(data) # 训练模型\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 详细输出原始数据及其类别\n",
    "r = pd.concat([data, pd.Series(model.labels_, index = data.index)], axis = 1) # 详细输出每个样本对应的类别\n",
    "r.columns = list(data.columns) + [u'聚类类别'] # 重命名表名\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure',figsize=(7,6))\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号\n",
    "\n",
    "style = ['ro-', 'go-', 'bo-']\n",
    "xlabels = [u'工作日人均停留时间', u'凌晨人均停留时间', u'周末人均停留时间', u'日均人流量']\n",
    "pic_output = 'type_'\n",
    "\n",
    "for i in range(k): # 逐一作图，作出不同样式\n",
    "    plt.figure()\n",
    "    tmp = r[r[u'聚类类别'] == i].iloc[:,:4] # 提取每一类\n",
    "    for j in range(len(tmp)):\n",
    "        plt.plot(range(1,5), tmp.iloc[j], style[i])\n",
    "    \n",
    "    plt.xticks(range(1,5), xlabels, rotation = 20) # 坐标标签 (***)\n",
    "    plt.subplots_adjust(bottom=0.15) # 调整底部 (***)\n",
    "    plt.savefig(u'%s%s.png' % (pic_output, i)) # 保存图片\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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